Summary

not sure if keeping this…

Introduction

Google relies on user proximity to provide local results for keywords. How strong is the proximity factor? How fast does the ranking decrease by distance from the location of a business?

The goals of the study are to try to estimate the drop in the ranking by geographical distance and to measure the variability due to the local context.

Methods

For this study, we focused on personal injury lawyers in major US cities. We collected 20 top ranking personal injury lawyers in each of the 10 largest cities.

For each of these law firm, we used the service Local Falcon to collect Google My Business rankings for listings that show up either in the Maps portion of the organic search, or from a search in the Google Maps Local Finder (i.e. Google Maps).

We collected their rankings for the keyword car accident lawyer at 225 locations on a 15x15 grid centered on their geographic location.

This is an example for the city of Miami:
example_miami_1

At the location of the law firm, it ranks 1st for the keyword car accident lawyer. Its ranking drops, however, as soon we are further away from its location. At the fringe of the grid, the law firm does not appear anymore in the top 20 (its exact ranking is not tracked anymore by Local Falcon).

This drop in the ranking can vary drastically between law firms, even in the same city. We see this variation if we flank our initial example with 2 other samples from Miami:
example_miami_2 example_miami_1 example_miami_3
On the left, we see a very quick drop in the ranking. On a the right, we see the case of a law firm which ranking does not drop much. The grid is always centered on the location of the target law firm.

To account for this high variation between the firm, we need to collect several samples in each city: 20. For 10 law firms, we used a radius of 5 miles, a finer granularity to better highlight the drop in ranking around the exact location of the firm. For 10 other law firms, we used a radius of 10 miles, to better identify the distance where most of the firms drop out of the top 20.

 

Most of the 200 law firms rank 1st at their own location (58%).

 

From the latitude and longitude of each of the 225 measurements on the 15x15 grid, we compute the geographical distance to the location of the target law firm. We then average the ranking of a law firm by mile to its own location.

For instance, with our previous example in Miami, we see that the law firm ranked first at its own location (distance = 0 miles). The ranking drops quickly, and the average position of all the measurements taken between 0 and 1 miles average to ~9. The average rank stabilizes then around 18.

We are then able to compute the average drop in the ranking in function of the distance to its initial ranking for any law firm.

— insert plot example with drop. —

Observations

Average drop by city:

Let’s have a look at all the samples in each city:

Let’s have a look at their original rank. By doing so, we might be able to differentiate the fate of top-ranking law firms from others.

Conclusion of the first data exploration

We sampled 20 law firms in the 10 largest cities (10 with a radius of 5 miles, and 10 with a radius of 10 miles). We found them by searching for ‘car accident lawyers’. For each, we measured their ranking for the keyword “car accident lawyer” at 225 location disposed on a squared grid of 5/10 miles “radius” around the original location of the firm.

Key observations:
1. The drop in ranking varies greatly between law firms. Some top-ranking firms do not even see a drop in the 5 miles. Some see a quick drop out of the top 20.
2. When there is a drop, much of the drop occurs in the first mile.
3. Only ~20% of the law firms dropped of the top 20 after 5 miles around their location.

some key statistics

average drop/mile

The average drop per mile (average for all cities):

Miles from location Average drop
1 -7.343700
2 -4.288252
3 -3.002659
4 -2.257771
5 -1.852558
6 -1.466885
7 -1.504112
8 -1.416301
9 -1.466052
10 -1.267415
11 -1.145147
12 -1.119136
13 -1.013824
14 -0.906500

Most of this drop is happening in the first mile(s).

Percentage out of the top20 per mile

Important, as one aim of our client is to be able to know as from which distance another law firm is no more in competition

Draw the grid for each city

Useful? We would be able to see the average pattern/grid, and see the drop in 2D, not in 1D. We would see that the drop is not always the same in all the directions.
Issue: not same location for the firms, so cannot really draw a localized average grid per city.

what else would be useful?

  • the drop in function of your initial position? It seems that if you start low, you drop faster out of the top 20, and that only law firms who
  • collect more data? (10 miles is already quite a lot. We can still collect 48 samples on a 15x15 grid.
  • start with top 10 analysis.
  • show the result in a .Rmd file, like an academic paper (methodology etc.). Short.